Overview

Dataset statistics

Number of variables41
Number of observations230892
Missing cells1689972
Missing cells (%)17.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.7 MiB
Average record size in memory180.2 B

Variable types

Numeric15
DateTime5
Categorical21

Alerts

S_sprache has constant value "Deutsch" Constant
Kommentar has a high cardinality: 60674 distinct values High cardinality
ft_tu has a high cardinality: 55 distinct values High cardinality
ft_vm has a high cardinality: 18468 distinct values High cardinality
fg_startort has a high cardinality: 12216 distinct values High cardinality
fg_zielort has a high cardinality: 12067 distinct values High cardinality
ft_startort has a high cardinality: 6278 distinct values High cardinality
ft_zielort has a high cardinality: 5387 distinct values High cardinality
wime_komfort is highly correlated with wime_sauberkeitHigh correlation
wime_sauberkeit is highly correlated with wime_komfortHigh correlation
wime_platzangebot is highly correlated with wime_komfort and 2 other fieldsHigh correlation
wime_gesamtzuf is highly correlated with wime_personal and 5 other fieldsHigh correlation
wime_preis_leistung is highly correlated with wime_gesamtzufHigh correlation
wime_personal is highly correlated with wime_gesamtzufHigh correlation
wime_puenktlich is highly correlated with wime_gesamtzufHigh correlation
wime_fahrplan is highly correlated with wime_gesamtzufHigh correlation
u_ga is highly correlated with S_AB3_HTA and 2 other fieldsHigh correlation
u_fahrausweis is highly correlated with S_AB3_HTA and 1 other fieldsHigh correlation
ft_tu is highly correlated with S_spracheHigh correlation
u_klassencode is highly correlated with S_spracheHigh correlation
R_anschluss is highly correlated with S_spracheHigh correlation
S_wohnsitz is highly correlated with S_spracheHigh correlation
R_zweck is highly correlated with S_spracheHigh correlation
S_AB3_HTA is highly correlated with u_ga and 2 other fieldsHigh correlation
dispcode is highly correlated with S_spracheHigh correlation
R_stoerung is highly correlated with S_spracheHigh correlation
u_ticket is highly correlated with u_ga and 1 other fieldsHigh correlation
ft_vm_kurz is highly correlated with S_spracheHigh correlation
device_type is highly correlated with S_spracheHigh correlation
S_sex is highly correlated with S_spracheHigh correlation
S_sprache is highly correlated with u_ga and 13 other fieldsHigh correlation
Kommentar has 168589 (73.0%) missing values Missing
wime_personal has 151584 (65.7%) missing values Missing
wime_komfort has 51326 (22.2%) missing values Missing
wime_sauberkeit has 48111 (20.8%) missing values Missing
wime_puenktlich has 47484 (20.6%) missing values Missing
wime_platzangebot has 46688 (20.2%) missing values Missing
wime_gesamtzuf has 39331 (17.0%) missing values Missing
wime_preis_leistung has 15536 (6.7%) missing values Missing
wime_fahrplan has 8377 (3.6%) missing values Missing
S_alter has 5871 (2.5%) missing values Missing
S_sex has 5571 (2.4%) missing values Missing
S_wohnsitz has 5570 (2.4%) missing values Missing
u_klassencode has 6082 (2.6%) missing values Missing
u_ga has 148510 (64.3%) missing values Missing
S_AB3_HTA has 12337 (5.3%) missing values Missing
R_anschluss has 102839 (44.5%) missing values Missing
R_stoerung has 48495 (21.0%) missing values Missing
device_type has 82382 (35.7%) missing values Missing
dispcode has 82382 (35.7%) missing values Missing
u_ticket has 40694 (17.6%) missing values Missing
u_fahrausweis has 111092 (48.1%) missing values Missing
u_preis has 22060 (9.6%) missing values Missing
R_zweck has 5518 (2.4%) missing values Missing
ft_abfahrt has 38933 (16.9%) missing values Missing
ft_ankunft has 38933 (16.9%) missing values Missing
ft_startort_uic has 38933 (16.9%) missing values Missing
ft_tu has 38933 (16.9%) missing values Missing
ft_vm has 38933 (16.9%) missing values Missing
ft_vm_kurz has 38933 (16.9%) missing values Missing
ft_zielort_uic has 38933 (16.9%) missing values Missing
fg_abfahrt has 28022 (12.1%) missing values Missing
fg_ankunft has 28022 (12.1%) missing values Missing
fg_startort_uic has 28022 (12.1%) missing values Missing
fg_zielort_uic has 28022 (12.1%) missing values Missing
fg_startort has 6756 (2.9%) missing values Missing
fg_zielort has 6753 (2.9%) missing values Missing
ft_startort has 17709 (7.7%) missing values Missing
ft_zielort has 17706 (7.7%) missing values Missing
u_preis is highly skewed (γ1 = 20.02315533) Skewed
ft_startort_uic is highly skewed (γ1 = -38.63050039) Skewed
fg_startort_uic is highly skewed (γ1 = -54.05818232) Skewed
fg_zielort_uic is highly skewed (γ1 = -45.69006732) Skewed
Kommentar is uniformly distributed Uniform
participant_id has unique values Unique
wime_komfort has 2958 (1.3%) zeros Zeros
wime_puenktlich has 4706 (2.0%) zeros Zeros
wime_platzangebot has 6412 (2.8%) zeros Zeros
wime_preis_leistung has 6492 (2.8%) zeros Zeros
wime_fahrplan has 5368 (2.3%) zeros Zeros

Reproduction

Analysis started2022-11-18 15:33:20.446976
Analysis finished2022-11-18 15:51:09.701201
Duration17 minutes and 49.25 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

participant_id
Real number (ℝ≥0)

UNIQUE

Distinct230892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375290.5297
Minimum642
Maximum589965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:09.791053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum642
5-th percentile66099.55
Q1339397.75
median428410.5
Q3508315.25
95-th percentile574149.35
Maximum589965
Range589323
Interquartile range (IQR)168917.5

Descriptive statistics

Standard deviation172177.6154
Coefficient of variation (CV)0.458784866
Kurtosis-0.78811269
Mean375290.5297
Median Absolute Deviation (MAD)84528
Skewness-0.7939403492
Sum8.665158098 × 1010
Variance2.964513124 × 1010
MonotonicityStrictly increasing
2022-11-18T16:51:09.891307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6421
 
< 0.1%
4809811
 
< 0.1%
4810261
 
< 0.1%
4810271
 
< 0.1%
4810281
 
< 0.1%
4810291
 
< 0.1%
4810301
 
< 0.1%
4810321
 
< 0.1%
4810331
 
< 0.1%
4810341
 
< 0.1%
Other values (230882)230882
> 99.9%
ValueCountFrequency (%)
6421
< 0.1%
6571
< 0.1%
247561
< 0.1%
256201
< 0.1%
412151
< 0.1%
413051
< 0.1%
413341
< 0.1%
413761
< 0.1%
414231
< 0.1%
414591
< 0.1%
ValueCountFrequency (%)
5899651
< 0.1%
5899621
< 0.1%
5899591
< 0.1%
5899571
< 0.1%
5899561
< 0.1%
5899551
< 0.1%
5899541
< 0.1%
5899511
< 0.1%
5899461
< 0.1%
5899451
< 0.1%

u_date
Date

Distinct1332
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2019-01-01 00:00:00
Maximum2022-11-16 00:00:00
2022-11-18T16:51:09.985836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:51:10.087015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kommentar
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct60674
Distinct (%)97.4%
Missing168589
Missing (%)73.0%
Memory size1.8 MiB
Nein
 
148
-
 
98
 
81
Keine
 
78
nein
 
68
Other values (60669)
61830 

Length

Max length2058
Median length1360
Mean length173.7266103
Min length0

Characters and Unicode

Total characters10823689
Distinct characters146
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60278 ?
Unique (%)96.7%

Sample

1st rowHabe schon mehrmals erlebt, dass es im Speisewagen keine Gipfeli gab am Morgen. Das war jeweils ärgerlich.
2nd rowAnsteben, dass auch in gut frequentierte periphere Zentren eine Fahrplanverdichtung erfolgt.Die Trennung von Fernverkehr und Regionalverkehr ist in Frage zu stellen. Schade, dass ich zu meiner ersten Fahrt am Tag Stellung nehmen muss, beginne ich dich mein Reiseprogramm aus Platzgründen im Zug bewusst früher als es nötig wäre...
3rd rowDie 1. Klasse muss deutluch aufgewertet werden. Die neuen untergeordneten Haltestellen wie Zürich Altstetten und Zürich Oerlikon trüben Fahrerlebnis massiv.
4th rowBessere (neue!) Züge in der Westschweiz!!! Längere Züge zwischen Bern und Genève (vor allem zwischen 16 Uhr und 19 Uhr) (mindestens +3 Wagen). Mehr 1. Klasse Gutscheine für 2.Klass-GA-Inhaber.
5th row- mehr Monitore - die Monitore so platzieren, dass man sie nicht suchen muss, wenn man knapp zum Bahnhof kommt, vor allem jetzt, wo alle Züge ständig auf anderen Gleisen fahren und die Wege sowieso

Common Values

ValueCountFrequency (%)
Nein148
 
0.1%
-98
 
< 0.1%
81
 
< 0.1%
Keine78
 
< 0.1%
nein68
 
< 0.1%
Mehr Sitzplätze40
 
< 0.1%
Pünktlichkeit36
 
< 0.1%
.35
 
< 0.1%
keine33
 
< 0.1%
Preise senken32
 
< 0.1%
Other values (60664)61654
 
26.7%
(Missing)168589
73.0%

Length

2022-11-18T16:51:10.198474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
die43712
 
2.6%
der36389
 
2.2%
ich35202
 
2.1%
und34875
 
2.1%
in26814
 
1.6%
nicht24892
 
1.5%
ist20596
 
1.2%
es20368
 
1.2%
das18697
 
1.1%
zu18555
 
1.1%
Other values (71888)1392143
83.3%

Most occurring characters

ValueCountFrequency (%)
1652663
15.3%
e1330460
 
12.3%
n849209
 
7.8%
i659547
 
6.1%
r580235
 
5.4%
s526374
 
4.9%
t517612
 
4.8%
a485579
 
4.5%
h414709
 
3.8%
d325183
 
3.0%
Other values (136)3482118
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8222895
76.0%
Space Separator1652687
 
15.3%
Uppercase Letter586937
 
5.4%
Other Punctuation249895
 
2.3%
Decimal Number60066
 
0.6%
Dash Punctuation24205
 
0.2%
Open Punctuation12262
 
0.1%
Close Punctuation12136
 
0.1%
Math Symbol1384
 
< 0.1%
Initial Punctuation924
 
< 0.1%
Other values (6)298
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1330460
16.2%
n849209
 
10.3%
i659547
 
8.0%
r580235
 
7.1%
s526374
 
6.4%
t517612
 
6.3%
a485579
 
5.9%
h414709
 
5.0%
d325183
 
4.0%
u323616
 
3.9%
Other values (40)2210371
26.9%
Uppercase Letter
ValueCountFrequency (%)
B64095
 
10.9%
S60209
 
10.3%
Z47700
 
8.1%
A45938
 
7.8%
D36329
 
6.2%
W26664
 
4.5%
M26662
 
4.5%
I25654
 
4.4%
P25400
 
4.3%
F24079
 
4.1%
Other values (24)204207
34.8%
Other Punctuation
ValueCountFrequency (%)
.137596
55.1%
,70092
28.0%
!20482
 
8.2%
?6992
 
2.8%
:6753
 
2.7%
/4412
 
1.8%
;1114
 
0.4%
'938
 
0.4%
593
 
0.2%
&368
 
0.1%
Other values (6)555
 
0.2%
Decimal Number
ValueCountFrequency (%)
114444
24.0%
211856
19.7%
010783
18.0%
35448
 
9.1%
54445
 
7.4%
43617
 
6.0%
72586
 
4.3%
62477
 
4.1%
92208
 
3.7%
82202
 
3.7%
Math Symbol
ValueCountFrequency (%)
>632
45.7%
+271
19.6%
=256
18.5%
<207
 
15.0%
×8
 
0.6%
~7
 
0.5%
|3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
(11546
94.2%
641
 
5.2%
67
 
0.5%
[7
 
0.1%
{1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
`36
63.2%
¨11
 
19.3%
´6
 
10.5%
^4
 
7.0%
Dash Punctuation
ValueCountFrequency (%)
-24003
99.2%
108
 
0.4%
94
 
0.4%
Close Punctuation
ValueCountFrequency (%)
)12124
99.9%
]8
 
0.1%
}4
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
639
69.2%
233
 
25.2%
«52
 
5.6%
Final Punctuation
ValueCountFrequency (%)
48
36.4%
»48
36.4%
36
27.3%
Space Separator
ValueCountFrequency (%)
1652663
> 99.9%
 24
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
10
90.9%
$1
 
9.1%
Other Number
ValueCountFrequency (%)
½7
87.5%
¼1
 
12.5%
Other Symbol
ValueCountFrequency (%)
°54
100.0%
Connector Punctuation
ValueCountFrequency (%)
_36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8809832
81.4%
Common2013857
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1330460
15.1%
n849209
 
9.6%
i659547
 
7.5%
r580235
 
6.6%
s526374
 
6.0%
t517612
 
5.9%
a485579
 
5.5%
h414709
 
4.7%
d325183
 
3.7%
u323616
 
3.7%
Other values (74)2797308
31.8%
Common
ValueCountFrequency (%)
1652663
82.1%
.137596
 
6.8%
,70092
 
3.5%
-24003
 
1.2%
!20482
 
1.0%
114444
 
0.7%
)12124
 
0.6%
211856
 
0.6%
(11546
 
0.6%
010783
 
0.5%
Other values (52)48268
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10653947
98.4%
None167266
 
1.5%
Punctuation2466
 
< 0.1%
Currency Symbols10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1652663
15.5%
e1330460
12.5%
n849209
 
8.0%
i659547
 
6.2%
r580235
 
5.4%
s526374
 
4.9%
t517612
 
4.9%
a485579
 
4.6%
h414709
 
3.9%
d325183
 
3.1%
Other values (83)3312376
31.1%
None
ValueCountFrequency (%)
ü81089
48.5%
ä55815
33.4%
ö26485
 
15.8%
Ö1294
 
0.8%
Ü827
 
0.5%
ß647
 
0.4%
Ä486
 
0.3%
é172
 
0.1%
è57
 
< 0.1%
°54
 
< 0.1%
Other values (32)340
 
0.2%
Punctuation
ValueCountFrequency (%)
641
26.0%
639
25.9%
593
24.0%
233
 
9.4%
108
 
4.4%
94
 
3.8%
67
 
2.7%
48
 
1.9%
36
 
1.5%
7
 
0.3%
Currency Symbols
ValueCountFrequency (%)
10
100.0%

wime_personal
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing151584
Missing (%)65.7%
Infinite0
Infinite (%)0.0%
Mean89.90300824
Minimum0
Maximum100
Zeros702
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:10.282347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q177.77777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.22222222

Descriptive statistics

Standard deviation17.81059679
Coefficient of variation (CV)0.1981090192
Kurtosis6.739073469
Mean89.90300824
Median Absolute Deviation (MAD)0
Skewness-2.363062916
Sum7130027.778
Variance317.2173579
MonotonicityNot monotonic
2022-11-18T16:51:10.356147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10052150
 
22.6%
759787
 
4.2%
88.888888895378
 
2.3%
77.777777784851
 
2.1%
66.666666671880
 
0.8%
501779
 
0.8%
44.44444444886
 
0.4%
55.55555556817
 
0.4%
0702
 
0.3%
25447
 
0.2%
Other values (3)631
 
0.3%
(Missing)151584
65.7%
ValueCountFrequency (%)
0702
 
0.3%
11.11111111138
 
0.1%
22.22222222228
 
0.1%
25447
 
0.2%
33.33333333265
 
0.1%
44.44444444886
 
0.4%
501779
 
0.8%
55.55555556817
 
0.4%
66.666666671880
 
0.8%
759787
4.2%
ValueCountFrequency (%)
10052150
22.6%
88.888888895378
 
2.3%
77.777777784851
 
2.1%
759787
 
4.2%
66.666666671880
 
0.8%
55.55555556817
 
0.4%
501779
 
0.8%
44.44444444886
 
0.4%
33.33333333265
 
0.1%
25447
 
0.2%

wime_komfort
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing51326
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean78.94154053
Minimum0
Maximum100
Zeros2958
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:10.426629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.33333333
Q175
median77.77777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.80887233
Coefficient of variation (CV)0.2889337119
Kurtosis1.479852277
Mean78.94154053
Median Absolute Deviation (MAD)22.22222222
Skewness-1.241166906
Sum14175216.67
Variance520.244657
MonotonicityNot monotonic
2022-11-18T16:51:10.498666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10068640
29.7%
7537780
16.4%
77.7777777817476
 
7.6%
88.8888888912656
 
5.5%
66.6666666710829
 
4.7%
5010475
 
4.5%
55.555555566008
 
2.6%
44.444444444853
 
2.1%
02958
 
1.3%
252840
 
1.2%
Other values (3)5051
 
2.2%
(Missing)51326
22.2%
ValueCountFrequency (%)
02958
 
1.3%
11.111111111003
 
0.4%
22.222222221686
 
0.7%
252840
 
1.2%
33.333333332362
 
1.0%
44.444444444853
 
2.1%
5010475
 
4.5%
55.555555566008
 
2.6%
66.6666666710829
 
4.7%
7537780
16.4%
ValueCountFrequency (%)
10068640
29.7%
88.8888888912656
 
5.5%
77.7777777817476
 
7.6%
7537780
16.4%
66.6666666710829
 
4.7%
55.555555566008
 
2.6%
5010475
 
4.5%
44.444444444853
 
2.1%
33.333333332362
 
1.0%
252840
 
1.2%

wime_sauberkeit
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing48111
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean79.2762316
Minimum0
Maximum100
Zeros1628
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:10.577821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.44444444
Q175
median77.77777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.45825872
Coefficient of variation (CV)0.2706770779
Kurtosis1.140392045
Mean79.2762316
Median Absolute Deviation (MAD)22.22222222
Skewness-1.10097625
Sum14490188.89
Variance460.4568672
MonotonicityNot monotonic
2022-11-18T16:51:10.652239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10067549
29.3%
7540603
17.6%
77.7777777818048
 
7.8%
88.8888888913669
 
5.9%
5012989
 
5.6%
66.6666666710791
 
4.7%
55.555555565597
 
2.4%
44.444444444517
 
2.0%
253289
 
1.4%
33.333333332172
 
0.9%
Other values (3)3557
 
1.5%
(Missing)48111
20.8%
ValueCountFrequency (%)
01628
 
0.7%
11.11111111606
 
0.3%
22.222222221323
 
0.6%
253289
 
1.4%
33.333333332172
 
0.9%
44.444444444517
 
2.0%
5012989
 
5.6%
55.555555565597
 
2.4%
66.6666666710791
 
4.7%
7540603
17.6%
ValueCountFrequency (%)
10067549
29.3%
88.8888888913669
 
5.9%
77.7777777818048
 
7.8%
7540603
17.6%
66.6666666710791
 
4.7%
55.555555565597
 
2.4%
5012989
 
5.6%
44.444444444517
 
2.0%
33.333333332172
 
0.9%
253289
 
1.4%

wime_puenktlich
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing47484
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean88.89655243
Minimum0
Maximum100
Zeros4706
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:10.723304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.33333333
Q188.88888889
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)11.11111111

Descriptive statistics

Standard deviation22.14660174
Coefficient of variation (CV)0.249127791
Kurtosis6.14107102
Mean88.89655243
Median Absolute Deviation (MAD)0
Skewness-2.50612666
Sum16304338.89
Variance490.4719684
MonotonicityNot monotonic
2022-11-18T16:51:10.795124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100126747
54.9%
7517854
 
7.7%
88.8888888911720
 
5.1%
77.777777787334
 
3.2%
04706
 
2.0%
504248
 
1.8%
66.666666672961
 
1.3%
252172
 
0.9%
44.444444441568
 
0.7%
55.555555561485
 
0.6%
Other values (3)2613
 
1.1%
(Missing)47484
 
20.6%
ValueCountFrequency (%)
04706
 
2.0%
11.11111111630
 
0.3%
22.22222222990
 
0.4%
252172
 
0.9%
33.33333333993
 
0.4%
44.444444441568
 
0.7%
504248
 
1.8%
55.555555561485
 
0.6%
66.666666672961
 
1.3%
7517854
7.7%
ValueCountFrequency (%)
100126747
54.9%
88.8888888911720
 
5.1%
77.777777787334
 
3.2%
7517854
 
7.7%
66.666666672961
 
1.3%
55.555555561485
 
0.6%
504248
 
1.8%
44.444444441568
 
0.7%
33.33333333993
 
0.4%
252172
 
0.9%

wime_platzangebot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing46688
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean80.20788848
Minimum0
Maximum100
Zeros6412
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:10.863485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.22222222
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation26.76140875
Coefficient of variation (CV)0.3336505829
Kurtosis1.370730952
Mean80.20788848
Median Absolute Deviation (MAD)0
Skewness-1.449757292
Sum14774613.89
Variance716.172998
MonotonicityNot monotonic
2022-11-18T16:51:10.937639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10093018
40.3%
7526450
 
11.5%
77.7777777812108
 
5.2%
88.8888888910362
 
4.5%
509950
 
4.3%
66.666666676883
 
3.0%
06412
 
2.8%
254655
 
2.0%
55.555555564103
 
1.8%
44.444444443944
 
1.7%
Other values (3)6319
 
2.7%
(Missing)46688
20.2%
ValueCountFrequency (%)
06412
 
2.8%
11.111111111554
 
0.7%
22.222222222323
 
1.0%
254655
 
2.0%
33.333333332442
 
1.1%
44.444444443944
 
1.7%
509950
 
4.3%
55.555555564103
 
1.8%
66.666666676883
 
3.0%
7526450
11.5%
ValueCountFrequency (%)
10093018
40.3%
88.8888888910362
 
4.5%
77.7777777812108
 
5.2%
7526450
 
11.5%
66.666666676883
 
3.0%
55.555555564103
 
1.8%
509950
 
4.3%
44.444444443944
 
1.7%
33.333333332442
 
1.1%
254655
 
2.0%

wime_gesamtzuf
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing39331
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean84.58357332
Minimum0
Maximum100
Zeros2073
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:11.013323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q175
median88.88888889
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.5786309
Coefficient of variation (CV)0.2314708416
Kurtosis3.737631081
Mean84.58357332
Median Absolute Deviation (MAD)11.11111111
Skewness-1.722164753
Sum16202913.89
Variance383.3227881
MonotonicityNot monotonic
2022-11-18T16:51:11.083717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10089625
38.8%
7539169
17.0%
88.8888888920408
 
8.8%
77.7777777816835
 
7.3%
507293
 
3.2%
66.666666676781
 
2.9%
55.555555562808
 
1.2%
44.444444442200
 
1.0%
02073
 
0.9%
251928
 
0.8%
Other values (3)2441
 
1.1%
(Missing)39331
17.0%
ValueCountFrequency (%)
02073
 
0.9%
11.11111111476
 
0.2%
22.22222222916
 
0.4%
251928
 
0.8%
33.333333331049
 
0.5%
44.444444442200
 
1.0%
507293
 
3.2%
55.555555562808
 
1.2%
66.666666676781
 
2.9%
7539169
17.0%
ValueCountFrequency (%)
10089625
38.8%
88.8888888920408
 
8.8%
77.7777777816835
 
7.3%
7539169
17.0%
66.666666676781
 
2.9%
55.555555562808
 
1.2%
507293
 
3.2%
44.444444442200
 
1.0%
33.333333331049
 
0.5%
251928
 
0.8%

wime_preis_leistung
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing15536
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean73.87228073
Minimum0
Maximum100
Zeros6492
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:11.154443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q150
median75
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation26.41526523
Coefficient of variation (CV)0.3575802042
Kurtosis0.2277605024
Mean73.87228073
Median Absolute Deviation (MAD)25
Skewness-0.9186382509
Sum15908838.89
Variance697.7662372
MonotonicityNot monotonic
2022-11-18T16:51:11.231898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10076907
33.3%
7546565
20.2%
5026078
 
11.3%
77.7777777813319
 
5.8%
66.6666666710095
 
4.4%
258623
 
3.7%
88.888888897822
 
3.4%
06492
 
2.8%
55.555555566301
 
2.7%
44.444444446265
 
2.7%
Other values (3)6889
 
3.0%
(Missing)15536
 
6.7%
ValueCountFrequency (%)
06492
 
2.8%
11.111111111224
 
0.5%
22.222222222597
 
1.1%
258623
 
3.7%
33.333333333068
 
1.3%
44.444444446265
 
2.7%
5026078
11.3%
55.555555566301
 
2.7%
66.6666666710095
 
4.4%
7546565
20.2%
ValueCountFrequency (%)
10076907
33.3%
88.888888897822
 
3.4%
77.7777777813319
 
5.8%
7546565
20.2%
66.6666666710095
 
4.4%
55.555555566301
 
2.7%
5026078
 
11.3%
44.444444446265
 
2.7%
33.333333333068
 
1.3%
258623
 
3.7%

wime_fahrplan
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing8377
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.40216764
Minimum0
Maximum100
Zeros5368
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:11.304271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.90631163
Coefficient of variation (CV)0.2866389724
Kurtosis2.541507494
Mean83.40216764
Median Absolute Deviation (MAD)0
Skewness-1.687869325
Sum18558233.33
Variance571.5117357
MonotonicityNot monotonic
2022-11-18T16:51:11.375543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100120898
52.4%
7533676
 
14.6%
77.7777777814716
 
6.4%
88.8888888912691
 
5.5%
5010927
 
4.7%
66.666666677761
 
3.4%
05368
 
2.3%
254482
 
1.9%
55.555555563971
 
1.7%
44.444444443752
 
1.6%
Other values (3)4273
 
1.9%
(Missing)8377
 
3.6%
ValueCountFrequency (%)
05368
 
2.3%
11.11111111843
 
0.4%
22.222222221512
 
0.7%
254482
 
1.9%
33.333333331918
 
0.8%
44.444444443752
 
1.6%
5010927
 
4.7%
55.555555563971
 
1.7%
66.666666677761
 
3.4%
7533676
14.6%
ValueCountFrequency (%)
100120898
52.4%
88.8888888912691
 
5.5%
77.7777777814716
 
6.4%
7533676
 
14.6%
66.666666677761
 
3.4%
55.555555563971
 
1.7%
5010927
 
4.7%
44.444444443752
 
1.6%
33.333333331918
 
0.8%
254482
 
1.9%

S_sprache
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.7 KiB
Deutsch
230892 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1616244
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeutsch
2nd rowDeutsch
3rd rowDeutsch
4th rowDeutsch
5th rowDeutsch

Common Values

ValueCountFrequency (%)
Deutsch230892
100.0%

Length

2022-11-18T16:51:11.453229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:11.532404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
deutsch230892
100.0%

Most occurring characters

ValueCountFrequency (%)
D230892
14.3%
e230892
14.3%
u230892
14.3%
t230892
14.3%
s230892
14.3%
c230892
14.3%
h230892
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1385352
85.7%
Uppercase Letter230892
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e230892
16.7%
u230892
16.7%
t230892
16.7%
s230892
16.7%
c230892
16.7%
h230892
16.7%
Uppercase Letter
ValueCountFrequency (%)
D230892
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1616244
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D230892
14.3%
e230892
14.3%
u230892
14.3%
t230892
14.3%
s230892
14.3%
c230892
14.3%
h230892
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1616244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D230892
14.3%
e230892
14.3%
u230892
14.3%
t230892
14.3%
s230892
14.3%
c230892
14.3%
h230892
14.3%

S_alter
Real number (ℝ≥0)

MISSING

Distinct89
Distinct (%)< 0.1%
Missing5871
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.62786584
Minimum10
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:11.610861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile23
Q140
median53
Q364
95-th percentile75
Maximum98
Range88
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.93758494
Coefficient of variation (CV)0.3087012155
Kurtosis-0.6204582656
Mean51.62786584
Median Absolute Deviation (MAD)12
Skewness-0.3072704461
Sum11617354
Variance254.0066138
MonotonicityNot monotonic
2022-11-18T16:51:11.708197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
556352
 
2.8%
606166
 
2.7%
505982
 
2.6%
585531
 
2.4%
565530
 
2.4%
575514
 
2.4%
525451
 
2.4%
655405
 
2.3%
545255
 
2.3%
535217
 
2.3%
Other values (79)168618
73.0%
(Missing)5871
 
2.5%
ValueCountFrequency (%)
1075
 
< 0.1%
1142
 
< 0.1%
1248
 
< 0.1%
1369
 
< 0.1%
14226
 
0.1%
15563
 
0.2%
161208
0.5%
171504
0.7%
181643
0.7%
191435
0.6%
ValueCountFrequency (%)
985
 
< 0.1%
972
 
< 0.1%
962
 
< 0.1%
959
 
< 0.1%
9410
 
< 0.1%
937
 
< 0.1%
928
 
< 0.1%
9126
< 0.1%
9064
< 0.1%
8954
< 0.1%

S_sex
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing5571
Missing (%)2.4%
Memory size225.7 KiB
weiblich
124194 
männlich
100457 
divers
 
670

Length

Max length8
Median length8
Mean length7.994052929
Min length6

Characters and Unicode

Total characters1801228
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmännlich
2nd rowmännlich
3rd rowweiblich
4th rowweiblich
5th rowweiblich

Common Values

ValueCountFrequency (%)
weiblich124194
53.8%
männlich100457
43.5%
divers670
 
0.3%
(Missing)5571
 
2.4%

Length

2022-11-18T16:51:11.796840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:11.879195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
weiblich124194
55.1%
männlich100457
44.6%
divers670
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i349515
19.4%
l224651
12.5%
c224651
12.5%
h224651
12.5%
n200914
11.2%
e124864
 
6.9%
w124194
 
6.9%
b124194
 
6.9%
m100457
 
5.6%
ä100457
 
5.6%
Other values (4)2680
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1801228
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i349515
19.4%
l224651
12.5%
c224651
12.5%
h224651
12.5%
n200914
11.2%
e124864
 
6.9%
w124194
 
6.9%
b124194
 
6.9%
m100457
 
5.6%
ä100457
 
5.6%
Other values (4)2680
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1801228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i349515
19.4%
l224651
12.5%
c224651
12.5%
h224651
12.5%
n200914
11.2%
e124864
 
6.9%
w124194
 
6.9%
b124194
 
6.9%
m100457
 
5.6%
ä100457
 
5.6%
Other values (4)2680
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700771
94.4%
None100457
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i349515
20.6%
l224651
13.2%
c224651
13.2%
h224651
13.2%
n200914
11.8%
e124864
 
7.3%
w124194
 
7.3%
b124194
 
7.3%
m100457
 
5.9%
d670
 
< 0.1%
Other values (3)2010
 
0.1%
None
ValueCountFrequency (%)
ä100457
100.0%

S_wohnsitz
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5570
Missing (%)2.4%
Memory size225.7 KiB
In der Schweiz / Liechtenstein
219959 
In einem anderen Land
 
5363

Length

Max length30
Median length30
Mean length29.78578656
Min length21

Characters and Unicode

Total characters6711393
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIn der Schweiz / Liechtenstein
2nd rowIn der Schweiz / Liechtenstein
3rd rowIn der Schweiz / Liechtenstein
4th rowIn der Schweiz / Liechtenstein
5th rowIn der Schweiz / Liechtenstein

Common Values

ValueCountFrequency (%)
In der Schweiz / Liechtenstein219959
95.3%
In einem anderen Land5363
 
2.3%
(Missing)5570
 
2.4%

Length

2022-11-18T16:51:11.945771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.017865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
in225322
20.1%
der219959
19.6%
schweiz219959
19.6%
219959
19.6%
liechtenstein219959
19.6%
einem5363
 
0.5%
anderen5363
 
0.5%
land5363
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e1121247
16.7%
895925
13.3%
n686692
10.2%
i665240
9.9%
c439918
 
6.6%
h439918
 
6.6%
t439918
 
6.6%
d230685
 
3.4%
I225322
 
3.4%
L225322
 
3.4%
Other values (8)1341206
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4924906
73.4%
Space Separator895925
 
13.3%
Uppercase Letter670603
 
10.0%
Other Punctuation219959
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1121247
22.8%
n686692
13.9%
i665240
13.5%
c439918
 
8.9%
h439918
 
8.9%
t439918
 
8.9%
d230685
 
4.7%
r225322
 
4.6%
s219959
 
4.5%
w219959
 
4.5%
Other values (3)236048
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
I225322
33.6%
L225322
33.6%
S219959
32.8%
Space Separator
ValueCountFrequency (%)
895925
100.0%
Other Punctuation
ValueCountFrequency (%)
/219959
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5595509
83.4%
Common1115884
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1121247
20.0%
n686692
12.3%
i665240
11.9%
c439918
 
7.9%
h439918
 
7.9%
t439918
 
7.9%
d230685
 
4.1%
I225322
 
4.0%
L225322
 
4.0%
r225322
 
4.0%
Other values (6)895925
16.0%
Common
ValueCountFrequency (%)
895925
80.3%
/219959
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6711393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1121247
16.7%
895925
13.3%
n686692
10.2%
i665240
9.9%
c439918
 
6.6%
h439918
 
6.6%
t439918
 
6.6%
d230685
 
3.4%
I225322
 
3.4%
L225322
 
3.4%
Other values (8)1341206
20.0%

u_klassencode
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6082
Missing (%)2.6%
Memory size225.7 KiB
2. Klasse
196209 
1. Klasse
28601 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters2023290
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2. Klasse
2nd row1. Klasse
3rd row2. Klasse
4th row2. Klasse
5th row2. Klasse

Common Values

ValueCountFrequency (%)
2. Klasse196209
85.0%
1. Klasse28601
 
12.4%
(Missing)6082
 
2.6%

Length

2022-11-18T16:51:12.080700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.162703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
klasse224810
50.0%
2196209
43.6%
128601
 
6.4%

Most occurring characters

ValueCountFrequency (%)
s449620
22.2%
.224810
11.1%
224810
11.1%
K224810
11.1%
l224810
11.1%
a224810
11.1%
e224810
11.1%
2196209
9.7%
128601
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1124050
55.6%
Other Punctuation224810
 
11.1%
Space Separator224810
 
11.1%
Uppercase Letter224810
 
11.1%
Decimal Number224810
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s449620
40.0%
l224810
20.0%
a224810
20.0%
e224810
20.0%
Decimal Number
ValueCountFrequency (%)
2196209
87.3%
128601
 
12.7%
Other Punctuation
ValueCountFrequency (%)
.224810
100.0%
Space Separator
ValueCountFrequency (%)
224810
100.0%
Uppercase Letter
ValueCountFrequency (%)
K224810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1348860
66.7%
Common674430
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s449620
33.3%
K224810
16.7%
l224810
16.7%
a224810
16.7%
e224810
16.7%
Common
ValueCountFrequency (%)
.224810
33.3%
224810
33.3%
2196209
29.1%
128601
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2023290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s449620
22.2%
.224810
11.1%
224810
11.1%
K224810
11.1%
l224810
11.1%
a224810
11.1%
e224810
11.1%
2196209
9.7%
128601
 
1.4%

u_ga
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing148510
Missing (%)64.3%
Memory size225.7 KiB
kein GA
75615 
besitzt GA
 
6767

Length

Max length10
Median length7
Mean length7.24642519
Min length7

Characters and Unicode

Total characters596975
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbesitzt GA
2nd rowbesitzt GA
3rd rowbesitzt GA
4th rowbesitzt GA
5th rowbesitzt GA

Common Values

ValueCountFrequency (%)
kein GA75615
32.7%
besitzt GA6767
 
2.9%
(Missing)148510
64.3%

Length

2022-11-18T16:51:12.234902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.316085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ga82382
50.0%
kein75615
45.9%
besitzt6767
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e82382
13.8%
i82382
13.8%
82382
13.8%
G82382
13.8%
A82382
13.8%
k75615
12.7%
n75615
12.7%
t13534
 
2.3%
b6767
 
1.1%
s6767
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter349829
58.6%
Uppercase Letter164764
27.6%
Space Separator82382
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e82382
23.5%
i82382
23.5%
k75615
21.6%
n75615
21.6%
t13534
 
3.9%
b6767
 
1.9%
s6767
 
1.9%
z6767
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
G82382
50.0%
A82382
50.0%
Space Separator
ValueCountFrequency (%)
82382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin514593
86.2%
Common82382
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e82382
16.0%
i82382
16.0%
G82382
16.0%
A82382
16.0%
k75615
14.7%
n75615
14.7%
t13534
 
2.6%
b6767
 
1.3%
s6767
 
1.3%
z6767
 
1.3%
Common
ValueCountFrequency (%)
82382
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII596975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e82382
13.8%
i82382
13.8%
82382
13.8%
G82382
13.8%
A82382
13.8%
k75615
12.7%
n75615
12.7%
t13534
 
2.3%
b6767
 
1.1%
s6767
 
1.1%

S_AB3_HTA
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing12337
Missing (%)5.3%
Memory size225.7 KiB
ja
180703 
nein
37852 

Length

Max length4
Median length2
Mean length2.346384205
Min length2

Characters and Unicode

Total characters512814
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowja
2nd rowja
3rd rowja
4th rowja
5th rowja

Common Values

ValueCountFrequency (%)
ja180703
78.3%
nein37852
 
16.4%
(Missing)12337
 
5.3%

Length

2022-11-18T16:51:12.391053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.478924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ja180703
82.7%
nein37852
 
17.3%

Most occurring characters

ValueCountFrequency (%)
j180703
35.2%
a180703
35.2%
n75704
14.8%
e37852
 
7.4%
i37852
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter512814
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
j180703
35.2%
a180703
35.2%
n75704
14.8%
e37852
 
7.4%
i37852
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin512814
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
j180703
35.2%
a180703
35.2%
n75704
14.8%
e37852
 
7.4%
i37852
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII512814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
j180703
35.2%
a180703
35.2%
n75704
14.8%
e37852
 
7.4%
i37852
 
7.4%

R_anschluss
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing102839
Missing (%)44.5%
Memory size225.7 KiB
Ja
122009 
Nein
 
6044

Length

Max length4
Median length2
Mean length2.094398413
Min length2

Characters and Unicode

Total characters268194
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJa
2nd rowJa
3rd rowJa
4th rowJa
5th rowJa

Common Values

ValueCountFrequency (%)
Ja122009
52.8%
Nein6044
 
2.6%
(Missing)102839
44.5%

Length

2022-11-18T16:51:12.555222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.635165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ja122009
95.3%
nein6044
 
4.7%

Most occurring characters

ValueCountFrequency (%)
J122009
45.5%
a122009
45.5%
N6044
 
2.3%
e6044
 
2.3%
i6044
 
2.3%
n6044
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter140141
52.3%
Uppercase Letter128053
47.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a122009
87.1%
e6044
 
4.3%
i6044
 
4.3%
n6044
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
J122009
95.3%
N6044
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin268194
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J122009
45.5%
a122009
45.5%
N6044
 
2.3%
e6044
 
2.3%
i6044
 
2.3%
n6044
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII268194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J122009
45.5%
a122009
45.5%
N6044
 
2.3%
e6044
 
2.3%
i6044
 
2.3%
n6044
 
2.3%

R_stoerung
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing48495
Missing (%)21.0%
Memory size225.7 KiB
Nein
168302 
Ja
 
14095

Length

Max length4
Median length4
Mean length3.845447019
Min length2

Characters and Unicode

Total characters701398
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNein
2nd rowNein
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein168302
72.9%
Ja14095
 
6.1%
(Missing)48495
 
21.0%

Length

2022-11-18T16:51:12.710413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.795500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nein168302
92.3%
ja14095
 
7.7%

Most occurring characters

ValueCountFrequency (%)
N168302
24.0%
e168302
24.0%
i168302
24.0%
n168302
24.0%
J14095
 
2.0%
a14095
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter519001
74.0%
Uppercase Letter182397
 
26.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e168302
32.4%
i168302
32.4%
n168302
32.4%
a14095
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
N168302
92.3%
J14095
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin701398
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N168302
24.0%
e168302
24.0%
i168302
24.0%
n168302
24.0%
J14095
 
2.0%
a14095
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII701398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N168302
24.0%
e168302
24.0%
i168302
24.0%
n168302
24.0%
J14095
 
2.0%
a14095
 
2.0%

device_type
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing82382
Missing (%)35.7%
Memory size225.7 KiB
Desktop
100249 
Smartphone
48261 

Length

Max length10
Median length7
Mean length7.974904047
Min length7

Characters and Unicode

Total characters1184353
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowDesktop

Common Values

ValueCountFrequency (%)
Desktop100249
43.4%
Smartphone48261
20.9%
(Missing)82382
35.7%

Length

2022-11-18T16:51:12.866226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:12.959147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop100249
67.5%
smartphone48261
32.5%

Most occurring characters

ValueCountFrequency (%)
e148510
12.5%
t148510
12.5%
o148510
12.5%
p148510
12.5%
D100249
8.5%
s100249
8.5%
k100249
8.5%
S48261
 
4.1%
m48261
 
4.1%
a48261
 
4.1%
Other values (3)144783
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1035843
87.5%
Uppercase Letter148510
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e148510
14.3%
t148510
14.3%
o148510
14.3%
p148510
14.3%
s100249
9.7%
k100249
9.7%
m48261
 
4.7%
a48261
 
4.7%
r48261
 
4.7%
h48261
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
D100249
67.5%
S48261
32.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1184353
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e148510
12.5%
t148510
12.5%
o148510
12.5%
p148510
12.5%
D100249
8.5%
s100249
8.5%
k100249
8.5%
S48261
 
4.1%
m48261
 
4.1%
a48261
 
4.1%
Other values (3)144783
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1184353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e148510
12.5%
t148510
12.5%
o148510
12.5%
p148510
12.5%
D100249
8.5%
s100249
8.5%
k100249
8.5%
S48261
 
4.1%
m48261
 
4.1%
a48261
 
4.1%
Other values (3)144783
12.2%

dispcode
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing82382
Missing (%)35.7%
Memory size225.7 KiB
Beendet
114032 
Ausgescreent
31492 
Beendet nach Unterbrechung
 
2986

Length

Max length26
Median length7
Mean length8.442286715
Min length7

Characters and Unicode

Total characters1253764
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBeendet
2nd rowBeendet
3rd rowBeendet
4th rowBeendet
5th rowBeendet

Common Values

ValueCountFrequency (%)
Beendet114032
49.4%
Ausgescreent31492
 
13.6%
Beendet nach Unterbrechung2986
 
1.3%
(Missing)82382
35.7%

Length

2022-11-18T16:51:13.034601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:13.123823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
beendet117018
75.7%
ausgescreent31492
 
20.4%
nach2986
 
1.9%
unterbrechung2986
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e451502
36.0%
n157468
 
12.6%
t151496
 
12.1%
B117018
 
9.3%
d117018
 
9.3%
s62984
 
5.0%
c37464
 
3.0%
r37464
 
3.0%
u34478
 
2.7%
g34478
 
2.7%
Other values (6)52394
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1096296
87.4%
Uppercase Letter151496
 
12.1%
Space Separator5972
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e451502
41.2%
n157468
 
14.4%
t151496
 
13.8%
d117018
 
10.7%
s62984
 
5.7%
c37464
 
3.4%
r37464
 
3.4%
u34478
 
3.1%
g34478
 
3.1%
h5972
 
0.5%
Other values (2)5972
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B117018
77.2%
A31492
 
20.8%
U2986
 
2.0%
Space Separator
ValueCountFrequency (%)
5972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1247792
99.5%
Common5972
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e451502
36.2%
n157468
 
12.6%
t151496
 
12.1%
B117018
 
9.4%
d117018
 
9.4%
s62984
 
5.0%
c37464
 
3.0%
r37464
 
3.0%
u34478
 
2.8%
g34478
 
2.8%
Other values (5)46422
 
3.7%
Common
ValueCountFrequency (%)
5972
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1253764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e451502
36.0%
n157468
 
12.6%
t151496
 
12.1%
B117018
 
9.3%
d117018
 
9.3%
s62984
 
5.0%
c37464
 
3.0%
r37464
 
3.0%
u34478
 
2.7%
g34478
 
2.7%
Other values (6)52394
 
4.2%

u_ticket
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing40694
Missing (%)17.6%
Memory size225.8 KiB
Mobile-Ticket
158212 
Online-Ticket
25204 
Easy Ride
 
6463
bedienter Vertrieb
 
319

Length

Max length18
Median length13
Mean length12.87246448
Min length9

Characters and Unicode

Total characters2448317
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile-Ticket
2nd rowMobile-Ticket
3rd rowMobile-Ticket
4th rowMobile-Ticket
5th rowMobile-Ticket

Common Values

ValueCountFrequency (%)
Mobile-Ticket158212
68.5%
Online-Ticket25204
 
10.9%
Easy Ride6463
 
2.8%
bedienter Vertrieb319
 
0.1%
(Missing)40694
 
17.6%

Length

2022-11-18T16:51:13.197648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:13.289111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
mobile-ticket158212
80.3%
online-ticket25204
 
12.8%
easy6463
 
3.3%
ride6463
 
3.3%
bedienter319
 
0.2%
vertrieb319
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e374890
15.3%
i373933
15.3%
t184054
7.5%
l183416
7.5%
-183416
7.5%
T183416
7.5%
c183416
7.5%
k183416
7.5%
b158850
6.5%
M158212
6.5%
Other values (12)281298
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1878042
76.7%
Uppercase Letter380077
 
15.5%
Dash Punctuation183416
 
7.5%
Space Separator6782
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e374890
20.0%
i373933
19.9%
t184054
9.8%
l183416
9.8%
c183416
9.8%
k183416
9.8%
b158850
8.5%
o158212
8.4%
n50727
 
2.7%
d6782
 
0.4%
Other values (4)20346
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
T183416
48.3%
M158212
41.6%
O25204
 
6.6%
E6463
 
1.7%
R6463
 
1.7%
V319
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-183416
100.0%
Space Separator
ValueCountFrequency (%)
6782
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2258119
92.2%
Common190198
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e374890
16.6%
i373933
16.6%
t184054
8.2%
l183416
8.1%
T183416
8.1%
c183416
8.1%
k183416
8.1%
b158850
7.0%
M158212
7.0%
o158212
7.0%
Other values (10)116304
 
5.2%
Common
ValueCountFrequency (%)
-183416
96.4%
6782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2448317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e374890
15.3%
i373933
15.3%
t184054
7.5%
l183416
7.5%
-183416
7.5%
T183416
7.5%
c183416
7.5%
k183416
7.5%
b158850
6.5%
M158212
6.5%
Other values (12)281298
11.5%

u_fahrausweis
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing111092
Missing (%)48.1%
Memory size226.0 KiB
Normales Billett
91847 
GA
15699 
Sparbillett
 
8084
Spartageskarte
 
3416
Tageskarte
 
459
Other values (2)
 
295

Length

Max length18
Median length16
Mean length13.74565109
Min length2

Characters and Unicode

Total characters1646729
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormales Billett
2nd rowNormales Billett
3rd rowNormales Billett
4th rowNormales Billett
5th rowNormales Billett

Common Values

ValueCountFrequency (%)
Normales Billett91847
39.8%
GA15699
 
6.8%
Sparbillett8084
 
3.5%
Spartageskarte3416
 
1.5%
Tageskarte459
 
0.2%
Strecken-/Modulabo216
 
0.1%
seven2579
 
< 0.1%
(Missing)111092
48.1%

Length

2022-11-18T16:51:13.368353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:13.463646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normales91847
43.4%
billett91847
43.4%
ga15699
 
7.4%
sparbillett8084
 
3.8%
spartageskarte3416
 
1.6%
tageskarte459
 
0.2%
strecken-/modulabo216
 
0.1%
seven2579
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l291925
17.7%
t207369
12.6%
e200118
12.2%
a111313
 
6.8%
r107438
 
6.5%
i99931
 
6.1%
s95801
 
5.8%
o92279
 
5.6%
N91847
 
5.6%
m91847
 
5.6%
Other values (20)256861
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1326809
80.6%
Uppercase Letter227483
 
13.8%
Space Separator91847
 
5.6%
Dash Punctuation216
 
< 0.1%
Other Punctuation216
 
< 0.1%
Decimal Number158
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l291925
22.0%
t207369
15.6%
e200118
15.1%
a111313
 
8.4%
r107438
 
8.1%
i99931
 
7.5%
s95801
 
7.2%
o92279
 
7.0%
m91847
 
6.9%
p11500
 
0.9%
Other values (8)17288
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
N91847
40.4%
B91847
40.4%
G15699
 
6.9%
A15699
 
6.9%
S11716
 
5.2%
T459
 
0.2%
M216
 
0.1%
Decimal Number
ValueCountFrequency (%)
279
50.0%
579
50.0%
Space Separator
ValueCountFrequency (%)
91847
100.0%
Dash Punctuation
ValueCountFrequency (%)
-216
100.0%
Other Punctuation
ValueCountFrequency (%)
/216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1554292
94.4%
Common92437
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l291925
18.8%
t207369
13.3%
e200118
12.9%
a111313
 
7.2%
r107438
 
6.9%
i99931
 
6.4%
s95801
 
6.2%
o92279
 
5.9%
N91847
 
5.9%
m91847
 
5.9%
Other values (15)164424
10.6%
Common
ValueCountFrequency (%)
91847
99.4%
-216
 
0.2%
/216
 
0.2%
279
 
0.1%
579
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1646729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l291925
17.7%
t207369
12.6%
e200118
12.2%
a111313
 
6.8%
r107438
 
6.5%
i99931
 
6.1%
s95801
 
5.8%
o92279
 
5.6%
N91847
 
5.6%
m91847
 
5.6%
Other values (20)256861
15.6%

u_preis
Real number (ℝ≥0)

MISSING
SKEWED

Distinct1452
Distinct (%)0.7%
Missing22060
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean33.47748717
Minimum0
Maximum6300
Zeros42
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:13.555904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.8
Q18.2
median16
Q329
95-th percentile61
Maximum6300
Range6300
Interquartile range (IQR)20.8

Descriptive statistics

Standard deviation203.8621595
Coefficient of variation (CV)6.089529913
Kurtosis450.3027534
Mean33.47748717
Median Absolute Deviation (MAD)9.2
Skewness20.02315533
Sum6991170.6
Variance41559.78009
MonotonicityNot monotonic
2022-11-18T16:51:13.656892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134891
 
2.1%
6.84786
 
2.1%
3.43974
 
1.7%
8.83028
 
1.3%
6.22834
 
1.2%
282461
 
1.1%
5.82425
 
1.1%
142393
 
1.0%
3.72358
 
1.0%
172344
 
1.0%
Other values (1442)177338
76.8%
(Missing)22060
 
9.6%
ValueCountFrequency (%)
042
 
< 0.1%
0.52
 
< 0.1%
1.32
 
< 0.1%
1.410
 
< 0.1%
1.57
 
< 0.1%
1.72
 
< 0.1%
2304
0.1%
2.14
 
< 0.1%
2.2396
0.2%
2.34
 
< 0.1%
ValueCountFrequency (%)
630055
 
< 0.1%
484022
 
< 0.1%
45201
 
< 0.1%
434011
 
< 0.1%
40504
 
< 0.1%
3860200
0.1%
35201
 
< 0.1%
2880130
0.1%
270075
 
< 0.1%
2650110
< 0.1%

R_zweck
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing5518
Missing (%)2.4%
Memory size225.7 KiB
Freizeit und Unterhaltung
150228 
Arbeit und Lernen
60015 
Sonstige
15131 

Length

Max length25
Median length25
Mean length21.72834045
Min length8

Characters and Unicode

Total characters4897003
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFreizeit und Unterhaltung
2nd rowFreizeit und Unterhaltung
3rd rowFreizeit und Unterhaltung
4th rowFreizeit und Unterhaltung
5th rowSonstige

Common Values

ValueCountFrequency (%)
Freizeit und Unterhaltung150228
65.1%
Arbeit und Lernen60015
 
26.0%
Sonstige15131
 
6.6%
(Missing)5518
 
2.4%

Length

2022-11-18T16:51:13.749934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T16:51:13.831306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
und210243
32.6%
freizeit150228
23.3%
unterhaltung150228
23.3%
arbeit60015
 
9.3%
lernen60015
 
9.3%
sonstige15131
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e645860
13.2%
n645860
13.2%
t525830
10.7%
420486
8.6%
r420486
8.6%
i375602
 
7.7%
u360471
 
7.4%
d210243
 
4.3%
g165359
 
3.4%
F150228
 
3.1%
Other values (11)976578
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4040900
82.5%
Uppercase Letter435617
 
8.9%
Space Separator420486
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e645860
16.0%
n645860
16.0%
t525830
13.0%
r420486
10.4%
i375602
9.3%
u360471
8.9%
d210243
 
5.2%
g165359
 
4.1%
a150228
 
3.7%
l150228
 
3.7%
Other values (5)390733
9.7%
Uppercase Letter
ValueCountFrequency (%)
F150228
34.5%
U150228
34.5%
A60015
 
13.8%
L60015
 
13.8%
S15131
 
3.5%
Space Separator
ValueCountFrequency (%)
420486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4476517
91.4%
Common420486
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e645860
14.4%
n645860
14.4%
t525830
11.7%
r420486
9.4%
i375602
8.4%
u360471
8.1%
d210243
 
4.7%
g165359
 
3.7%
F150228
 
3.4%
a150228
 
3.4%
Other values (10)826350
18.5%
Common
ValueCountFrequency (%)
420486
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4897003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e645860
13.2%
n645860
13.2%
t525830
10.7%
420486
8.6%
r420486
8.6%
i375602
 
7.7%
u360471
 
7.4%
d210243
 
4.3%
g165359
 
3.4%
F150228
 
3.1%
Other values (11)976578
19.9%

ft_abfahrt
Date

MISSING

Distinct1318
Distinct (%)0.7%
Missing38933
Missing (%)16.9%
Memory size1.8 MiB
Minimum2022-11-18 00:00:00
Maximum2022-11-18 23:59:00
2022-11-18T16:51:13.917173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:51:14.013122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ft_ankunft
Date

MISSING

Distinct1333
Distinct (%)0.7%
Missing38933
Missing (%)16.9%
Memory size1.8 MiB
Minimum2022-11-18 00:00:00
Maximum2022-11-18 23:59:00
2022-11-18T16:51:14.113688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:51:14.214982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ft_startort_uic
Real number (ℝ≥0)

MISSING
SKEWED

Distinct2367
Distinct (%)1.2%
Missing38933
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean8508828.455
Minimum1101961
Maximum8891702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:14.313862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1101961
5-th percentile8500023
Q18503000
median8504300
Q38507000
95-th percentile8576333
Maximum8891702
Range7789741
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation51739.0053
Coefficient of variation (CV)0.006080626208
Kurtosis4633.854583
Mean8508828.455
Median Absolute Deviation (MAD)2170
Skewness-38.63050039
Sum1.633346201 × 1012
Variance2676924670
MonotonicityNot monotonic
2022-11-18T16:51:14.417214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300020574
 
8.9%
850700011488
 
5.0%
85000107525
 
3.3%
85050006952
 
3.0%
85030165678
 
2.5%
85002185377
 
2.3%
85060004285
 
1.9%
85063023131
 
1.4%
85090002445
 
1.1%
85022042405
 
1.0%
Other values (2357)122099
52.9%
(Missing)38933
 
16.9%
ValueCountFrequency (%)
11019612
 
< 0.1%
51038651
 
< 0.1%
55100172
 
< 0.1%
800107118
< 0.1%
80010931
 
< 0.1%
80020841
 
< 0.1%
80021401
 
< 0.1%
80021813
 
< 0.1%
80022533
 
< 0.1%
80023012
 
< 0.1%
ValueCountFrequency (%)
88917021
 
< 0.1%
88120051
 
< 0.1%
87751001
 
< 0.1%
87746871
 
< 0.1%
87745493
 
< 0.1%
87715131
 
< 0.1%
87713045
< 0.1%
87182064
 
< 0.1%
859600410
< 0.1%
85959311
 
< 0.1%

ft_tu
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct55
Distinct (%)< 0.1%
Missing38933
Missing (%)16.9%
Memory size228.1 KiB
SBB
149841 
BLS
 
10611
SOB
 
7661
THU
 
6981
RhB
 
4508
Other values (50)
 
12357

Length

Max length3
Median length3
Mean length2.973035909
Min length1

Characters and Unicode

Total characters570701
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowTHU
2nd rowSBB
3rd rowSBB
4th rowSBB
5th rowSBB

Common Values

ValueCountFrequency (%)
SBB149841
64.9%
BLS10611
 
4.6%
SOB7661
 
3.3%
THU6981
 
3.0%
RhB4508
 
2.0%
ZB3441
 
1.5%
MGB1732
 
0.8%
AVA1127
 
0.5%
RBS1023
 
0.4%
AB-990
 
0.4%
Other values (45)4044
 
1.8%
(Missing)38933
 
16.9%

Length

2022-11-18T16:51:14.515150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sbb149841
78.1%
bls10611
 
5.5%
sob7661
 
4.0%
thu6981
 
3.6%
rhb4508
 
2.3%
zb3443
 
1.8%
mgb1732
 
0.9%
ava1127
 
0.6%
rbs1023
 
0.5%
ab990
 
0.5%
Other values (43)4042
 
2.1%

Most occurring characters

ValueCountFrequency (%)
B331598
58.1%
S170098
29.8%
L10738
 
1.9%
O8191
 
1.4%
U7559
 
1.3%
T7455
 
1.3%
H6981
 
1.2%
R6289
 
1.1%
h4508
 
0.8%
A4377
 
0.8%
Other values (21)12907
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter565077
99.0%
Lowercase Letter4625
 
0.8%
Dash Punctuation992
 
0.2%
Connector Punctuation7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B331598
58.7%
S170098
30.1%
L10738
 
1.9%
O8191
 
1.4%
U7559
 
1.3%
T7455
 
1.3%
H6981
 
1.2%
R6289
 
1.1%
A4377
 
0.8%
Z4021
 
0.7%
Other values (15)7770
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
h4508
97.5%
e65
 
1.4%
r42
 
0.9%
t10
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-992
100.0%
Connector Punctuation
ValueCountFrequency (%)
_7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin569702
99.8%
Common999
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B331598
58.2%
S170098
29.9%
L10738
 
1.9%
O8191
 
1.4%
U7559
 
1.3%
T7455
 
1.3%
H6981
 
1.2%
R6289
 
1.1%
h4508
 
0.8%
A4377
 
0.8%
Other values (19)11908
 
2.1%
Common
ValueCountFrequency (%)
-992
99.3%
_7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII570651
> 99.9%
None50
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B331598
58.1%
S170098
29.8%
L10738
 
1.9%
O8191
 
1.4%
U7559
 
1.3%
T7455
 
1.3%
H6981
 
1.2%
R6289
 
1.1%
h4508
 
0.8%
A4377
 
0.8%
Other values (19)12857
 
2.3%
None
ValueCountFrequency (%)
Ö49
98.0%
Ü1
 
2.0%

ft_vm
Categorical

HIGH CARDINALITY
MISSING

Distinct18468
Distinct (%)9.6%
Missing38933
Missing (%)16.9%
Memory size1.1 MiB
IC 5
 
578
IC 8
 
462
IC 8 808
 
437
IC 1
 
416
IC 8 825
 
405
Other values (18463)
189661 

Length

Max length14
Median length13
Mean length8.927109435
Min length3

Characters and Unicode

Total characters1713639
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6318 ?
Unique (%)3.3%

Sample

1st rowS 30
2nd rowIR 36 2057
3rd rowIR 75 2128
4th rowIC 8 817
5th rowS 25 8540

Common Values

ValueCountFrequency (%)
IC 5578
 
0.3%
IC 8462
 
0.2%
IC 8 808437
 
0.2%
IC 1416
 
0.2%
IC 8 825405
 
0.2%
IC 8 827403
 
0.2%
IC 8 829381
 
0.2%
IC 8 826362
 
0.2%
IC 8 810358
 
0.2%
IC 8 806339
 
0.1%
Other values (18458)187818
81.3%
(Missing)38933
 
16.9%

Length

2022-11-18T16:51:14.603800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s53462
 
11.0%
ic44019
 
9.1%
ir40160
 
8.3%
513778
 
2.8%
812750
 
2.6%
111076
 
2.3%
310282
 
2.1%
re8806
 
1.8%
756606
 
1.4%
r5525
 
1.1%
Other values (14677)278346
57.4%

Most occurring characters

ValueCountFrequency (%)
309866
18.1%
1199529
11.6%
2153729
 
9.0%
I112723
 
6.6%
5110413
 
6.4%
3104107
 
6.1%
693684
 
5.5%
888644
 
5.2%
785452
 
5.0%
R69137
 
4.0%
Other values (13)386355
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1012831
59.1%
Uppercase Letter323936
 
18.9%
Space Separator309866
 
18.1%
Dash Punctuation67006
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I112723
34.8%
R69137
21.3%
C65799
20.3%
S56511
17.4%
E18042
 
5.6%
G530
 
0.2%
T519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L33
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1199529
19.7%
2153729
15.2%
5110413
10.9%
3104107
10.3%
693684
9.2%
888644
8.8%
785452
8.4%
464069
 
6.3%
057245
 
5.7%
955959
 
5.5%
Space Separator
ValueCountFrequency (%)
309866
100.0%
Dash Punctuation
ValueCountFrequency (%)
-67006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1389703
81.1%
Latin323936
 
18.9%

Most frequent character per script

Common
ValueCountFrequency (%)
309866
22.3%
1199529
14.4%
2153729
11.1%
5110413
 
7.9%
3104107
 
7.5%
693684
 
6.7%
888644
 
6.4%
785452
 
6.1%
-67006
 
4.8%
464069
 
4.6%
Other values (2)113204
 
8.1%
Latin
ValueCountFrequency (%)
I112723
34.8%
R69137
21.3%
C65799
20.3%
S56511
17.4%
E18042
 
5.6%
G530
 
0.2%
T519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1713639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309866
18.1%
1199529
11.6%
2153729
 
9.0%
I112723
 
6.6%
5110413
 
6.4%
3104107
 
6.1%
693684
 
5.5%
888644
 
5.2%
785452
 
5.0%
R69137
 
4.0%
Other values (13)386355
22.5%

ft_vm_kurz
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)< 0.1%
Missing38933
Missing (%)16.9%
Memory size226.0 KiB
IC
57871 
S
56397 
IR
52150 
RE
10061 
R
6886 
Other values (7)
8594 

Length

Max length3
Median length2
Mean length1.687110268
Min length1

Characters and Unicode

Total characters323856
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS
2nd rowIR
3rd rowIR
4th rowIC
5th rowS

Common Values

ValueCountFrequency (%)
IC57871
25.1%
S56397
24.4%
IR52150
22.6%
RE10061
 
4.4%
R6886
 
3.0%
EC5251
 
2.3%
ICE2677
 
1.2%
TGV519
 
0.2%
SN113
 
< 0.1%
IRE25
 
< 0.1%
Other values (2)9
 
< 0.1%
(Missing)38933
16.9%

Length

2022-11-18T16:51:14.693091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ic57871
30.1%
s56397
29.4%
ir52150
27.2%
re10061
 
5.2%
r6886
 
3.6%
ec5251
 
2.7%
ice2677
 
1.4%
tgv519
 
0.3%
sn113
 
0.1%
ire25
 
< 0.1%
Other values (2)9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I112723
34.8%
R69122
21.3%
C65799
20.3%
S56511
17.4%
E18022
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter323856
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I112723
34.8%
R69122
21.3%
C65799
20.3%
S56511
17.4%
E18022
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin323856
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I112723
34.8%
R69122
21.3%
C65799
20.3%
S56511
17.4%
E18022
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII323856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I112723
34.8%
R69122
21.3%
C65799
20.3%
S56511
17.4%
E18022
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

ft_zielort_uic
Real number (ℝ≥0)

MISSING

Distinct1467
Distinct (%)0.8%
Missing38933
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean8500541.988
Minimum5501362
Maximum8814001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:14.791026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5501362
5-th percentile8500023
Q18503000
median8503127
Q38506290
95-th percentile8509000
Maximum8814001
Range3312639
Interquartile range (IQR)3290

Descriptive statistics

Standard deviation42612.15621
Coefficient of variation (CV)0.005012875211
Kurtosis611.2885544
Mean8500541.988
Median Absolute Deviation (MAD)1873
Skewness-16.79147258
Sum1.63175554 × 1012
Variance1815795857
MonotonicityNot monotonic
2022-11-18T16:51:14.903859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300035898
 
15.5%
850700017517
 
7.6%
850500010178
 
4.4%
85002188388
 
3.6%
85000107389
 
3.2%
85060005514
 
2.4%
85063024686
 
2.0%
85090003810
 
1.7%
85030163807
 
1.6%
85021132900
 
1.3%
Other values (1457)91872
39.8%
(Missing)38933
16.9%
ValueCountFrequency (%)
55013621
 
< 0.1%
55100173
 
< 0.1%
800107115
< 0.1%
80010931
 
< 0.1%
80021401
 
< 0.1%
80021813
 
< 0.1%
80022533
 
< 0.1%
80023012
 
< 0.1%
80023071
 
< 0.1%
80023717
< 0.1%
ValueCountFrequency (%)
88140011
 
< 0.1%
87746471
 
< 0.1%
87746001
 
< 0.1%
87745496
< 0.1%
87745381
 
< 0.1%
87725681
 
< 0.1%
87723191
 
< 0.1%
87715131
 
< 0.1%
87713045
< 0.1%
87688881
 
< 0.1%

fg_abfahrt
Date

MISSING

Distinct1342
Distinct (%)0.7%
Missing28022
Missing (%)12.1%
Memory size1.8 MiB
Minimum2022-11-18 00:00:00
Maximum2022-11-18 23:59:00
2022-11-18T16:51:15.002212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:51:15.102621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fg_ankunft
Date

MISSING

Distinct1366
Distinct (%)0.7%
Missing28022
Missing (%)12.1%
Memory size1.8 MiB
Minimum2022-11-18 00:00:00
Maximum2022-11-18 23:59:00
2022-11-18T16:51:15.198671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:51:15.296865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fg_startort_uic
Real number (ℝ≥0)

MISSING
SKEWED

Distinct10869
Distinct (%)5.4%
Missing28022
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean8509955.155
Minimum1101316
Maximum8891702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:15.395131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1101316
5-th percentile8500026
Q18503000
median8505000
Q38507493
95-th percentile8587757
Maximum8891702
Range7790386
Interquartile range (IQR)4493

Descriptive statistics

Standard deviation108480.6597
Coefficient of variation (CV)0.01274750074
Kurtosis3631.64314
Mean8509955.155
Median Absolute Deviation (MAD)2172
Skewness-54.05818232
Sum1.726414602 × 1012
Variance1.176805352 × 1010
MonotonicityNot monotonic
2022-11-18T16:51:15.499814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300013420
 
5.8%
85070008471
 
3.7%
85000106968
 
3.0%
85030165884
 
2.5%
85050005851
 
2.5%
85060003338
 
1.4%
85002182603
 
1.1%
85063022543
 
1.1%
85021132041
 
0.9%
85071001972
 
0.9%
Other values (10859)149779
64.9%
(Missing)28022
 
12.1%
ValueCountFrequency (%)
11013161
 
< 0.1%
11013271
 
< 0.1%
11019541
 
< 0.1%
11019571
 
< 0.1%
11020341
 
< 0.1%
11049351
 
< 0.1%
110649327
< 0.1%
14018101
 
< 0.1%
51038651
 
< 0.1%
55100172
 
< 0.1%
ValueCountFrequency (%)
88917021
 
< 0.1%
88120051
 
< 0.1%
87763021
 
< 0.1%
87751001
 
< 0.1%
87746872
 
< 0.1%
87746003
< 0.1%
87745642
 
< 0.1%
87745494
< 0.1%
87742321
 
< 0.1%
87723197
< 0.1%

fg_zielort_uic
Real number (ℝ≥0)

MISSING
SKEWED

Distinct10663
Distinct (%)5.3%
Missing28022
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean8507601.845
Minimum1101322
Maximum8831138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-18T16:51:15.604584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1101322
5-th percentile8500023
Q18503000
median8505000
Q38507483
95-th percentile8588580
Maximum8831138
Range7729816
Interquartile range (IQR)4483

Descriptive statistics

Standard deviation133401.7265
Coefficient of variation (CV)0.01568029733
Kurtosis2494.239821
Mean8507601.845
Median Absolute Deviation (MAD)2060
Skewness-45.69006732
Sum1.725937186 × 1012
Variance1.779602064 × 1010
MonotonicityNot monotonic
2022-11-18T16:51:15.709383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300018423
 
8.0%
850700010209
 
4.4%
85050007003
 
3.0%
85000106795
 
2.9%
85030164944
 
2.1%
85060004175
 
1.8%
85063023592
 
1.6%
85002182735
 
1.2%
85021132503
 
1.1%
85071001977
 
0.9%
Other values (10653)140514
60.9%
(Missing)28022
 
12.1%
ValueCountFrequency (%)
11013221
 
< 0.1%
11013231
 
< 0.1%
11015351
 
< 0.1%
11018942
 
< 0.1%
11019573
 
< 0.1%
11019662
 
< 0.1%
11021382
 
< 0.1%
11025021
 
< 0.1%
110649331
< 0.1%
11100001
 
< 0.1%
ValueCountFrequency (%)
88311381
 
< 0.1%
88140011
 
< 0.1%
87747002
 
< 0.1%
87746871
 
< 0.1%
87746471
 
< 0.1%
87746001
 
< 0.1%
87745641
 
< 0.1%
87745591
 
< 0.1%
87745498
< 0.1%
87745381
 
< 0.1%

fg_startort
Categorical

HIGH CARDINALITY
MISSING

Distinct12216
Distinct (%)5.5%
Missing6756
Missing (%)2.9%
Memory size804.6 KiB
Zürich HB
 
14422
Bern
 
9076
Basel SBB
 
7575
Luzern
 
6284
Zürich Flughafen
 
6253
Other values (12211)
180526 

Length

Max length33
Median length26
Mean length11.27582361
Min length1

Characters and Unicode

Total characters2527318
Distinct characters92
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3876 ?
Unique (%)1.7%

Sample

1st rowFrauenfeld
2nd rowRheinfelden
3rd rowAmriswil
4th rowSalgesch
5th rowDottikon-Dintikon

Common Values

ValueCountFrequency (%)
Zürich HB14422
 
6.2%
Bern9076
 
3.9%
Basel SBB7575
 
3.3%
Luzern6284
 
2.7%
Zürich Flughafen6253
 
2.7%
Winterthur3637
 
1.6%
Olten2829
 
1.2%
St. Gallen2694
 
1.2%
Aarau2162
 
0.9%
Thun2091
 
0.9%
Other values (12206)167113
72.4%
(Missing)6756
 
2.9%

Length

2022-11-18T16:51:15.816316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich28139
 
8.1%
hb14656
 
4.2%
bern11178
 
3.2%
basel9054
 
2.6%
sbb7860
 
2.3%
luzern7198
 
2.1%
flughafen6385
 
1.8%
st5081
 
1.5%
winterthur5002
 
1.4%
dorf4283
 
1.2%
Other values (9237)247116
71.4%

Most occurring characters

ValueCountFrequency (%)
e248248
 
9.8%
n197727
 
7.8%
r181216
 
7.2%
i139261
 
5.5%
a138840
 
5.5%
121876
 
4.8%
l120336
 
4.8%
t112677
 
4.5%
h109391
 
4.3%
s95130
 
3.8%
Other values (82)1062616
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1915433
75.8%
Uppercase Letter420484
 
16.6%
Space Separator121876
 
4.8%
Other Punctuation55736
 
2.2%
Dash Punctuation9958
 
0.4%
Close Punctuation1813
 
0.1%
Open Punctuation1813
 
0.1%
Decimal Number198
 
< 0.1%
Math Symbol7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e248248
13.0%
n197727
10.3%
r181216
 
9.5%
i139261
 
7.3%
a138840
 
7.2%
l120336
 
6.3%
t112677
 
5.9%
h109391
 
5.7%
s95130
 
5.0%
o79831
 
4.2%
Other values (31)492776
25.7%
Uppercase Letter
ValueCountFrequency (%)
B82188
19.5%
S49056
11.7%
Z39947
 
9.5%
H29110
 
6.9%
L23617
 
5.6%
G23259
 
5.5%
W20258
 
4.8%
A18550
 
4.4%
F16162
 
3.8%
R14755
 
3.5%
Other values (23)103582
24.6%
Decimal Number
ValueCountFrequency (%)
475
37.9%
631
15.7%
327
 
13.6%
025
 
12.6%
117
 
8.6%
212
 
6.1%
710
 
5.1%
81
 
0.5%
Other Punctuation
ValueCountFrequency (%)
,43278
77.6%
.7769
 
13.9%
/4541
 
8.1%
'147
 
0.3%
&1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
121876
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9958
100.0%
Close Punctuation
ValueCountFrequency (%)
)1813
100.0%
Open Punctuation
ValueCountFrequency (%)
(1813
100.0%
Math Symbol
ValueCountFrequency (%)
+7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2335917
92.4%
Common191401
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e248248
 
10.6%
n197727
 
8.5%
r181216
 
7.8%
i139261
 
6.0%
a138840
 
5.9%
l120336
 
5.2%
t112677
 
4.8%
h109391
 
4.7%
s95130
 
4.1%
B82188
 
3.5%
Other values (64)910903
39.0%
Common
ValueCountFrequency (%)
121876
63.7%
,43278
 
22.6%
-9958
 
5.2%
.7769
 
4.1%
/4541
 
2.4%
)1813
 
0.9%
(1813
 
0.9%
'147
 
0.1%
475
 
< 0.1%
631
 
< 0.1%
Other values (8)100
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2470536
97.8%
None56782
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e248248
 
10.0%
n197727
 
8.0%
r181216
 
7.3%
i139261
 
5.6%
a138840
 
5.6%
121876
 
4.9%
l120336
 
4.9%
t112677
 
4.6%
h109391
 
4.4%
s95130
 
3.9%
Other values (60)1005834
40.7%
None
ValueCountFrequency (%)
ü41959
73.9%
ä7448
 
13.1%
ö3883
 
6.8%
è1317
 
2.3%
é1090
 
1.9%
â470
 
0.8%
Ü369
 
0.6%
Ä95
 
0.2%
ô40
 
0.1%
Ö34
 
0.1%
Other values (12)77
 
0.1%

fg_zielort
Categorical

HIGH CARDINALITY
MISSING

Distinct12067
Distinct (%)5.4%
Missing6753
Missing (%)2.9%
Memory size803.4 KiB
Zürich HB
19804 
Bern
 
10880
Luzern
 
7522
Basel SBB
 
7448
Zürich Flughafen
 
5243
Other values (12062)
173242 

Length

Max length40
Median length28
Mean length11.1858445
Min length2

Characters and Unicode

Total characters2507184
Distinct characters95
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4142 ?
Unique (%)1.8%

Sample

1st rowSeuzach
2nd rowLugano
3rd rowRichterswil
4th rowInterlaken West
5th rowBrugg AG

Common Values

ValueCountFrequency (%)
Zürich HB19804
 
8.6%
Bern10880
 
4.7%
Luzern7522
 
3.3%
Basel SBB7448
 
3.2%
Zürich Flughafen5243
 
2.3%
Winterthur4487
 
1.9%
St. Gallen3785
 
1.6%
Olten2954
 
1.3%
Aarau2687
 
1.2%
Chur2119
 
0.9%
Other values (12057)157210
68.1%
(Missing)6753
 
2.9%

Length

2022-11-18T16:51:15.918976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich38182
 
10.9%
hb20173
 
5.7%
bern13795
 
3.9%
basel9089
 
2.6%
luzern8872
 
2.5%
sbb7693
 
2.2%
st6620
 
1.9%
winterthur5501
 
1.6%
flughafen5458
 
1.6%
gallen5296
 
1.5%
Other values (9216)230890
65.7%

Most occurring characters

ValueCountFrequency (%)
e234871
 
9.4%
n188958
 
7.5%
r187026
 
7.5%
i139905
 
5.6%
a138828
 
5.5%
127539
 
5.1%
l117759
 
4.7%
h114990
 
4.6%
t109425
 
4.4%
s89858
 
3.6%
Other values (85)1058025
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1881422
75.0%
Uppercase Letter429479
 
17.1%
Space Separator127539
 
5.1%
Other Punctuation56020
 
2.2%
Dash Punctuation8415
 
0.3%
Open Punctuation2031
 
0.1%
Close Punctuation2031
 
0.1%
Decimal Number227
 
< 0.1%
Math Symbol20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e234871
12.5%
n188958
 
10.0%
r187026
 
9.9%
i139905
 
7.4%
a138828
 
7.4%
l117759
 
6.3%
h114990
 
6.1%
t109425
 
5.8%
s89858
 
4.8%
u77691
 
4.1%
Other values (33)482111
25.6%
Uppercase Letter
ValueCountFrequency (%)
B89516
20.8%
Z48797
11.4%
S48509
11.3%
H32873
 
7.7%
L24615
 
5.7%
G22696
 
5.3%
W18636
 
4.3%
A18043
 
4.2%
F14404
 
3.4%
R13610
 
3.2%
Other values (22)97780
22.8%
Decimal Number
ValueCountFrequency (%)
477
33.9%
330
 
13.2%
626
 
11.5%
124
 
10.6%
022
 
9.7%
221
 
9.3%
719
 
8.4%
84
 
1.8%
92
 
0.9%
52
 
0.9%
Other Punctuation
ValueCountFrequency (%)
,41563
74.2%
.8923
 
15.9%
/5413
 
9.7%
'119
 
0.2%
&2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
127539
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8415
100.0%
Open Punctuation
ValueCountFrequency (%)
(2031
100.0%
Close Punctuation
ValueCountFrequency (%)
)2031
100.0%
Math Symbol
ValueCountFrequency (%)
+20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2310901
92.2%
Common196283
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e234871
 
10.2%
n188958
 
8.2%
r187026
 
8.1%
i139905
 
6.1%
a138828
 
6.0%
l117759
 
5.1%
h114990
 
5.0%
t109425
 
4.7%
s89858
 
3.9%
B89516
 
3.9%
Other values (65)899765
38.9%
Common
ValueCountFrequency (%)
127539
65.0%
,41563
 
21.2%
.8923
 
4.5%
-8415
 
4.3%
/5413
 
2.8%
(2031
 
1.0%
)2031
 
1.0%
'119
 
0.1%
477
 
< 0.1%
330
 
< 0.1%
Other values (10)142
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2444050
97.5%
None63134
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e234871
 
9.6%
n188958
 
7.7%
r187026
 
7.7%
i139905
 
5.7%
a138828
 
5.7%
127539
 
5.2%
l117759
 
4.8%
h114990
 
4.7%
t109425
 
4.5%
s89858
 
3.7%
Other values (62)994891
40.7%
None
ValueCountFrequency (%)
ü49929
79.1%
ä6239
 
9.9%
ö3525
 
5.6%
è1289
 
2.0%
é1008
 
1.6%
â470
 
0.7%
Ü433
 
0.7%
ô55
 
0.1%
Ä54
 
0.1%
à29
 
< 0.1%
Other values (13)103
 
0.2%

ft_startort
Categorical

HIGH CARDINALITY
MISSING

Distinct6278
Distinct (%)2.9%
Missing17709
Missing (%)7.7%
Memory size629.2 KiB
Zürich HB
21568 
Bern
 
12089
Basel SBB
 
8129
Luzern
 
7383
Zürich Flughafen
 
6046
Other values (6273)
157968 

Length

Max length31
Median length27
Mean length10.29527683
Min length1

Characters and Unicode

Total characters2194778
Distinct characters88
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2885 ?
Unique (%)1.4%

Sample

1st rowFrauenfeld
2nd rowRheinfelden
3rd rowWeinfelden
4th rowVisp
5th rowDottikon-Dintikon

Common Values

ValueCountFrequency (%)
Zürich HB21568
 
9.3%
Bern12089
 
5.2%
Basel SBB8129
 
3.5%
Luzern7383
 
3.2%
Zürich Flughafen6046
 
2.6%
Olten5601
 
2.4%
Winterthur4583
 
2.0%
St. Gallen3282
 
1.4%
Chur2597
 
1.1%
Zug2536
 
1.1%
Other values (6268)139369
60.4%
(Missing)17709
 
7.7%

Length

2022-11-18T16:51:16.018353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich34135
 
10.6%
hb22124
 
6.9%
bahnhof20703
 
6.5%
bern14141
 
4.4%
basel9805
 
3.1%
sbb9524
 
3.0%
luzern8594
 
2.7%
flughafen6486
 
2.0%
olten6015
 
1.9%
winterthur5850
 
1.8%
Other values (4869)183599
57.2%

Most occurring characters

ValueCountFrequency (%)
e185834
 
8.5%
n183188
 
8.3%
r150707
 
6.9%
h137129
 
6.2%
a128829
 
5.9%
B113630
 
5.2%
i112865
 
5.1%
107817
 
4.9%
l94467
 
4.3%
t84871
 
3.9%
Other values (78)895441
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1626932
74.1%
Uppercase Letter406511
 
18.5%
Space Separator107817
 
4.9%
Other Punctuation43896
 
2.0%
Dash Punctuation7677
 
0.3%
Open Punctuation892
 
< 0.1%
Close Punctuation892
 
< 0.1%
Decimal Number100
 
< 0.1%
Math Symbol61
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e185834
11.4%
n183188
11.3%
r150707
 
9.3%
h137129
 
8.4%
a128829
 
7.9%
i112865
 
6.9%
l94467
 
5.8%
t84871
 
5.2%
o74860
 
4.6%
u74039
 
4.6%
Other values (28)400143
24.6%
Uppercase Letter
ValueCountFrequency (%)
B113630
28.0%
Z44790
 
11.0%
S43970
 
10.8%
H33492
 
8.2%
L22322
 
5.5%
G19859
 
4.9%
W17840
 
4.4%
A14127
 
3.5%
F14121
 
3.5%
O12317
 
3.0%
Other values (23)70043
17.2%
Decimal Number
ValueCountFrequency (%)
445
45.0%
612
 
12.0%
311
 
11.0%
111
 
11.0%
710
 
10.0%
210
 
10.0%
01
 
1.0%
Other Punctuation
ValueCountFrequency (%)
,30977
70.6%
.6695
 
15.3%
/6169
 
14.1%
'31
 
0.1%
&24
 
0.1%
Space Separator
ValueCountFrequency (%)
107817
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7677
100.0%
Open Punctuation
ValueCountFrequency (%)
(892
100.0%
Close Punctuation
ValueCountFrequency (%)
)892
100.0%
Math Symbol
ValueCountFrequency (%)
+61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2033443
92.6%
Common161335
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e185834
 
9.1%
n183188
 
9.0%
r150707
 
7.4%
h137129
 
6.7%
a128829
 
6.3%
B113630
 
5.6%
i112865
 
5.6%
l94467
 
4.6%
t84871
 
4.2%
o74860
 
3.7%
Other values (61)767063
37.7%
Common
ValueCountFrequency (%)
107817
66.8%
,30977
 
19.2%
-7677
 
4.8%
.6695
 
4.1%
/6169
 
3.8%
(892
 
0.6%
)892
 
0.6%
+61
 
< 0.1%
445
 
< 0.1%
'31
 
< 0.1%
Other values (7)79
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2142555
97.6%
None52223
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e185834
 
8.7%
n183188
 
8.5%
r150707
 
7.0%
h137129
 
6.4%
a128829
 
6.0%
B113630
 
5.3%
i112865
 
5.3%
107817
 
5.0%
l94467
 
4.4%
t84871
 
4.0%
Other values (59)843218
39.4%
None
ValueCountFrequency (%)
ü42850
82.1%
ä4731
 
9.1%
ö1527
 
2.9%
è1182
 
2.3%
é889
 
1.7%
â523
 
1.0%
Ü368
 
0.7%
Ä82
 
0.2%
Ö24
 
< 0.1%
ì14
 
< 0.1%
Other values (9)33
 
0.1%

ft_zielort
Categorical

HIGH CARDINALITY
MISSING

Distinct5387
Distinct (%)2.5%
Missing17706
Missing (%)7.7%
Memory size622.2 KiB
Zürich HB
37265 
Bern
18180 
Luzern
 
10693
Olten
 
8603
Basel SBB
 
8041
Other values (5382)
130404 

Length

Max length40
Median length29
Mean length8.796956648
Min length3

Characters and Unicode

Total characters1875388
Distinct characters85
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2808 ?
Unique (%)1.3%

Sample

1st rowOberwinterthur
2nd rowZürich HB
3rd rowZürich HB
4th rowSpiez
5th rowBrugg AG

Common Values

ValueCountFrequency (%)
Zürich HB37265
 
16.1%
Bern18180
 
7.9%
Luzern10693
 
4.6%
Olten8603
 
3.7%
Basel SBB8041
 
3.5%
Winterthur5826
 
2.5%
St. Gallen4879
 
2.1%
Zürich Flughafen4106
 
1.8%
Chur3962
 
1.7%
Aarau3083
 
1.3%
Other values (5377)108548
47.0%
(Missing)17706
 
7.7%

Length

2022-11-18T16:51:16.124330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich50440
 
16.7%
hb37808
 
12.5%
bern19113
 
6.3%
luzern11013
 
3.6%
olten8697
 
2.9%
basel8477
 
2.8%
sbb8234
 
2.7%
st6163
 
2.0%
winterthur6087
 
2.0%
gallen5445
 
1.8%
Other values (4522)140362
46.5%

Most occurring characters

ValueCountFrequency (%)
e166360
 
8.9%
r159331
 
8.5%
n142842
 
7.6%
i112933
 
6.0%
B101246
 
5.4%
h97110
 
5.2%
88685
 
4.7%
a86023
 
4.6%
l84489
 
4.5%
t72527
 
3.9%
Other values (75)763842
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1369493
73.0%
Uppercase Letter393323
 
21.0%
Space Separator88685
 
4.7%
Other Punctuation15825
 
0.8%
Dash Punctuation6397
 
0.3%
Open Punctuation767
 
< 0.1%
Close Punctuation767
 
< 0.1%
Decimal Number127
 
< 0.1%
Math Symbol4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e166360
12.1%
r159331
11.6%
n142842
10.4%
i112933
 
8.2%
h97110
 
7.1%
a86023
 
6.3%
l84489
 
6.2%
t72527
 
5.3%
c66870
 
4.9%
u64257
 
4.7%
Other values (27)316751
23.1%
Uppercase Letter
ValueCountFrequency (%)
B101246
25.7%
Z58847
15.0%
H44746
11.4%
S36949
 
9.4%
L22163
 
5.6%
G18481
 
4.7%
W14736
 
3.7%
O14668
 
3.7%
A13272
 
3.4%
R9748
 
2.5%
Other values (22)58467
14.9%
Decimal Number
ValueCountFrequency (%)
437
29.1%
720
15.7%
620
15.7%
320
15.7%
220
15.7%
110
 
7.9%
Other Punctuation
ValueCountFrequency (%)
.6934
43.8%
,4738
29.9%
/4131
26.1%
'20
 
0.1%
&2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
88685
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6397
100.0%
Open Punctuation
ValueCountFrequency (%)
(767
100.0%
Close Punctuation
ValueCountFrequency (%)
)767
100.0%
Math Symbol
ValueCountFrequency (%)
+4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1762816
94.0%
Common112572
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e166360
 
9.4%
r159331
 
9.0%
n142842
 
8.1%
i112933
 
6.4%
B101246
 
5.7%
h97110
 
5.5%
a86023
 
4.9%
l84489
 
4.8%
t72527
 
4.1%
c66870
 
3.8%
Other values (59)673085
38.2%
Common
ValueCountFrequency (%)
88685
78.8%
.6934
 
6.2%
-6397
 
5.7%
,4738
 
4.2%
/4131
 
3.7%
(767
 
0.7%
)767
 
0.7%
437
 
< 0.1%
'20
 
< 0.1%
720
 
< 0.1%
Other values (6)76
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1811584
96.6%
None63804
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e166360
 
9.2%
r159331
 
8.8%
n142842
 
7.9%
i112933
 
6.2%
B101246
 
5.6%
h97110
 
5.4%
88685
 
4.9%
a86023
 
4.7%
l84489
 
4.7%
t72527
 
4.0%
Other values (58)700038
38.6%
None
ValueCountFrequency (%)
ü56870
89.1%
ä3231
 
5.1%
ö1022
 
1.6%
è933
 
1.5%
é697
 
1.1%
â526
 
0.8%
Ü429
 
0.7%
Ä37
 
0.1%
Ö19
 
< 0.1%
ô9
 
< 0.1%
Other values (7)31
 
< 0.1%

Interactions

2022-11-18T16:46:53.388852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:53.340089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:18.594928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:32.294788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.053420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.124169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.176568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:11.889771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:35.527186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:02.857117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:30.542589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:51.842973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:21.125165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:15.964730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:51.951928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:47:01.186264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:53.446423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:18.688269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:32.391623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.149377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.215319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.268930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.020463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:35.640275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:02.951316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:30.635241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:51.932120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:22.844321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:17.068858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:59.728792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:47:05.822711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:53.571302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:18.789916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:32.498686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.256577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.321422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.376316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.165233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:35.770969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.062923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:30.747189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.035478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:23.732680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:17.734463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:43:04.186587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:47:12.421572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:53.681091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:18.879404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:32.602794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.359427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.432558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.482049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.305406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:35.915291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.172584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:30.857784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.139792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:25.430610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:18.917943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:43:26.458096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:47:18.971922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:53.787686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:18.967376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:32.707784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.462976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.537977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.588335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.436267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:36.059854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.283081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:30.966501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.242413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:27.158517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:20.045213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:43:33.071270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:47:25.728451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:53.892576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:19.054325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:32.816882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.565558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.641324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.691860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.569646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:36.182666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.395217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:31.075621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.345920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:28.891299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:43:40.154071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:47:32.289829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:37:32.926485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.667436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.742891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.795567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.696234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:36.315094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.502153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:31.184096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.451792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:30.612384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:38:18.845575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:41.898340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.819016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:36.447044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.614287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:31.300665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.562065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:41:32.268277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:23.417259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:43:54.701589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:49:51.275532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:36:54.223150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:19.317613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:33.138927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:54.875129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:18.947525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:42.002529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:12.964710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:39:36.584516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:03.728791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:31.419962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:40:52.669034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:38:19.051286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:40:03.854206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:37:19.495744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:33.347586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:37:55.133731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:38:19.149846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:39:13.293395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:40:53.009488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:37:20.636271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-18T16:41:54.823910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:42:43.063080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-18T16:46:36.158550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-18T16:51:16.215510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-18T16:51:16.331361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-18T16:51:16.443289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-18T16:51:16.564442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-11-18T16:51:04.243889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-18T16:51:05.441966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-18T16:51:08.308402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-18T16:51:09.264231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

participant_idu_dateKommentarwime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanS_spracheS_alterS_sexS_wohnsitzu_klassencodeu_gaS_AB3_HTAR_anschlussR_stoerungdevice_typedispcodeu_ticketu_fahrausweisu_preisR_zweckft_abfahrtft_ankunftft_startort_uicft_tuft_vmft_vm_kurzft_zielort_uicfg_abfahrtfg_ankunftfg_startort_uicfg_zielort_uicfg_startortfg_zielortft_startortft_zielort
06422019-01-19NoneNaN88.88888988.888889100.000000100.00000088.888889NaN100.000000DeutschNaNmännlichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-18 17:58:002022-11-18 18:10:008506100THUS 30S85060162022-11-18 17:58:002022-11-18 18:22:0085061008506020FrauenfeldSeuzachFrauenfeldOberwinterthur
16572019-03-02None100.000000100.000000100.000000100.000000100.000000100.000000NaN100.000000Deutsch68.0männlichIn der Schweiz / Liechtenstein1. Klassebesitzt GANaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-18 05:50:002022-11-18 06:49:008500301SBBIR 36 2057IR85030002022-11-18 05:50:002022-11-18 09:18:0085003018505300RheinfeldenLuganoRheinfeldenZürich HB
2247562019-04-22NoneNaN66.66666766.666667100.00000066.66666777.777778NaN100.000000Deutsch25.0weiblichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-18 16:36:002022-11-18 17:25:008506105SBBIR 75 2128IR85030002022-11-18 16:22:002022-11-18 18:10:0085061098503207AmriswilRichterswilWeinfeldenZürich HB
3256202019-01-05None88.88888944.44444466.666667100.00000044.44444477.777778NaN77.777778Deutsch16.0weiblichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-18 10:57:002022-11-18 11:24:008501605SBBIC 8 817IC85074832022-11-18 10:28:002022-11-18 11:51:0085016008507493SalgeschInterlaken WestVispSpiez
4412152019-01-12NoneNaN88.88888988.888889100.000000100.00000088.888889NaN100.000000Deutsch72.0weiblichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNNaNNeinNaNNaNNaNNaNNaNSonstige2022-11-18 11:10:002022-11-18 11:25:008502212SBBS 25 8540S85003092022-11-18 11:10:002022-11-18 11:25:0085022128500309Dottikon-DintikonBrugg AGDottikon-DintikonBrugg AG
5413052019-01-04Habe schon mehrmals erlebt, dass es im Speisewagen keine Gipfeli gab am Morgen. Das war jeweils ärgerlich.77.77777844.44444477.77777877.77777877.77777888.888889NaN66.666667Deutsch24.0weiblichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNNaNNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-18 16:57:002022-11-18 17:25:008502206SBBIR 75 2663IR85050002022-11-18 16:57:002022-11-18 17:25:0085022068505000BaarLuzernBaarLuzern
6413342019-02-01Ansteben, dass auch in gut frequentierte periphere Zentren eine Fahrplanverdichtung erfolgt.Die Trennung von Fernverkehr und Regionalverkehr ist in Frage zu stellen. Schade, dass ich zu meiner ersten Fahrt am Tag Stellung nehmen muss, beginne ich dich mein Reiseprogramm aus Platzgründen im Zug bewusst früher als es nötig wäre...100.00000088.888889100.000000100.00000088.88888988.888889NaN100.000000Deutsch43.0männlichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNNaNNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-18 05:50:002022-11-18 06:24:008507483SBBIC 61 1056IC85070002022-11-18 05:50:002022-11-18 06:24:0085074838507000SpiezBernSpiezBern
7413762019-02-24NoneNaN66.66666755.555556100.00000077.77777888.888889NaN66.666667Deutsch21.0weiblichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNNaNNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-18 04:56:002022-11-18 05:51:008506208SBBIR 13 3252IR85030002022-11-18 04:56:002022-11-18 05:51:0085062088503000UzwilZürich HBUzwilZürich HB
8414232019-01-06None100.000000100.000000100.000000100.000000100.000000100.000000NaN100.000000Deutsch35.0weiblichIn der Schweiz / Liechtenstein2. Klassebesitzt GANaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-18 17:41:002022-11-18 19:28:008505213SBBIC 2 884IC85030002022-11-18 17:05:002022-11-18 20:58:0085054008507000LocarnoBernBellinzonaZürich HB
9414592019-01-07Die 1. Klasse muss deutluch aufgewertet werden. Die neuen untergeordneten Haltestellen wie Zürich Altstetten und Zürich Oerlikon trüben Fahrerlebnis massiv.NaN0.00000066.66666755.55555633.33333333.333333NaN33.333333Deutsch47.0männlichIn der Schweiz / Liechtenstein1. Klassebesitzt GANaNJaNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-18 07:12:002022-11-18 08:06:008508100SBBIR 17 2359IR85030002022-11-18 07:12:002022-11-18 08:38:0085081008506000LangenthalWinterthurLangenthalZürich HB

Last rows

participant_idu_dateKommentarwime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanS_spracheS_alterS_sexS_wohnsitzu_klassencodeu_gaS_AB3_HTAR_anschlussR_stoerungdevice_typedispcodeu_ticketu_fahrausweisu_preisR_zweckft_abfahrtft_ankunftft_startort_uicft_tuft_vmft_vm_kurzft_zielort_uicfg_abfahrtfg_ankunftfg_startort_uicfg_zielort_uicfg_startortfg_zielortft_startortft_zielort
2308825899452022-11-14None100.0100.075.0100.0100.0100.075.075.0Deutsch75.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNjaJaNeinDesktopBeendetMobile-TicketNormales Billett18.1Freizeit und Unterhaltung2022-11-18 14:58:002022-11-18 15:57:008506302SBBIC-5-528IC85030002022-11-18 14:31:002022-11-18 15:57:0085063668503000SpeicherZürich HBSt. GallenZürich HB
2308835899462022-11-07Die WC sind leider häufig sehr sehr schmutzig. Ich fahre diese Strecke öfters.NaNNaNNaNNaNNaNNaN75.0100.0Deutsch71.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNjaNaNNaNDesktopAusgescreentNaNSparbillett26.4SonstigeNaTNaTNaNNaNNaNNaNNaNNaTNaTNaNNaNLiestalChurLiestalChur
2308845899512022-11-13NoneNaN75.075.075.025.075.050.075.0Deutsch45.0männlichIn der Schweiz / Liechtenstein2. KlasseNaNjaJaNeinDesktopBeendetMobile-TicketNormales Billett24.5Freizeit und Unterhaltung2022-11-18 18:04:002022-11-18 19:13:008503000SBBIC-5-532IC85043002022-11-18 18:04:002022-11-18 19:23:0085030008576724Zürich HBBiel/Bienne, OmegaZürich HBBiel/Bienne
2308855899542022-11-11None100.0100.0100.0100.0100.0100.0100.0100.0Deutsch49.0männlichIn der Schweiz / Liechtenstein2. KlasseNaNjaJaNeinDesktopBeendetNaNSparbillett22.6Freizeit und Unterhaltung2022-11-18 07:02:002022-11-18 07:58:008507000SBBIC-8-807IC85030002022-11-18 06:39:002022-11-18 08:12:0085044108503016ZollikofenZürich FlughafenBernZürich HB
2308865899552022-11-11NoneNaN75.075.0100.0100.075.0100.0100.0Deutsch43.0männlichIn der Schweiz / Liechtenstein2. KlasseNaNjaJaNeinDesktopBeendetNaNSparbillett23.4Freizeit und Unterhaltung2022-11-18 12:31:002022-11-18 13:28:008507000SBBIC-1-717IC85030002022-11-18 11:40:002022-11-18 13:28:0085041948503000Laupen BEZürich HBBernZürich HB
2308875899562022-11-13None0.0NaNNaN100.0100.025.050.00.0DeutschNaNdiversIn der Schweiz / Liechtenstein2. KlasseNaNneinJaNeinDesktopBeendetNaNSparbillett20.2Freizeit und Unterhaltung2022-11-18 15:42:002022-11-18 16:24:008500305SBBIR-36-1977IR85030002022-11-18 15:42:002022-11-18 16:43:0085003058591183FrickZürich, HelmhausFrickZürich HB
2308885899572022-11-13NoneNaN100.0100.075.075.0100.075.0100.0Deutsch48.0weiblichIn der Schweiz / Liechtenstein1. KlasseNaNjaJaNeinDesktopBeendetMobile-TicketNormales Billett57.0Freizeit und Unterhaltung2022-11-18 15:34:002022-11-18 16:58:008507100SBBIC-8-825IC85030002022-11-18 15:34:002022-11-18 17:22:0085071008503400ThunBülachThunZürich HB
2308895899592022-11-11Ich hatte meinen grossen Hund (Deutsche Dogge) dabei und war sehr zufrieden mit unserem Platz im Spielwagon unten, freie Fläche zum liegen für den Hund und dennoch eine Sitzgelegenheit für mich. Der Zugbegleiter war grandios.100.0100.075.0100.0100.0100.075.0100.0Deutsch38.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNneinJaNeinDesktopBeendetNaNSparbillett34.2Freizeit und Unterhaltung2022-11-18 09:36:002022-11-18 10:32:008507000SBBIC-61-1062IC85000102022-11-18 09:04:002022-11-18 10:56:0085041058578157NiederwangenBasel, NeuweilerstrasseBernBasel SBB
2308905899622022-11-13Die nahtlosen Anschlüsse habe ich sehr geschätzt, sodass ich rasch von Basel nach Hause kam. Sicherheitspersonal oder Kondukteure im Zug erhöhen das Sicherheitsgefühl positiv. Die Züge donnerstags abends um 22.36 Uhr sind stets überfüllt! Bitte mehr Wagen anhängen. Die Reinigung der Züge ist unterschiedlich - die Fahrgäste und ihr Verhalten wohl leider auch.NaN75.050.0100.0100.075.075.0100.0Deutsch61.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNjaJaNeinDesktopBeendetMobile-TicketNormales Billett12.9Freizeit und Unterhaltung2022-11-18 17:11:002022-11-18 17:57:008500010SBBIR-36-1981IR85003092022-11-18 17:11:002022-11-18 18:12:0085000108503777Basel SBBUntersiggenthal, SpiracherBasel SBBBrugg AG
2308915899652022-11-12None100.075.0100.0100.0100.0100.0100.0100.0Deutsch64.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNjaJaNeinDesktopBeendetNaNSparbillett27.4Freizeit und Unterhaltung2022-11-18 09:29:002022-11-18 11:22:008502204SBBIR-70-2616IR85063022022-11-18 07:59:002022-11-18 11:22:0085082118506302SchüpfheimSt. GallenZugSt. Gallen